TopasOpt: An open-source library for optimization with Topas Monte Carlo.

PURPOSE: To describe and test TopasOpt: a free, open-source and extensible library for performing mathematical optimization of Monte Carlo simulations in Topas. METHODS: TopasOpt enables any Topas model to be transformed into an optimization problem, and any parameter within the model to be treated as an optimization variable. Three case studies are presented. The starting model consists of a 10 MeV electron beam striking a tungsten target. The resulting bremsstrahlung X-ray spectrum is collimated by a primary and secondary collimator before being scored in a water tank. In the first case study (electron phase space optimization), five parameters describing the electron beam were treated as optimization variables and assigned a random starting value. An objective function was defined based on differences of depth-dose and profiles in water between the original (ground truth) model and a given model generated by TopasOpt. The problem was solved using Bayesian Optimization and the Nelder-Mead method. One hundred iterations were run in each case. In the second case study, (collimator geometry optimization), this process was repeated, but three geometric parameters defining the secondary collimator were treated as optimization variables and assigned random starting values, and forty iterations were run. In the third case study, the optimization was repeated with different number of primary particles to study the effect of noise on convergence. RESULTS: For case 1 (phase space optimization), both optimization algorithms successfully minimized the objective function, with absolute mean differences in profile dose of 0.4% (Bayesian) and 0.3% (Nelder-Mead) and 0.2% in depth-dose for both algorithms. The beam energy was recovered to within 1%, however some parameters had relative errors of up to 171% - a result consistent with the known X-ray dose is insensitivity to many electron beam parameters. For case 2 (geometry optimization), absolute mean differences in profile dose were 0.6% (Bayesian) and 0.9% (Nelder-Mead), and 0.5% and 0.9% in depth-dose. The maximum percentage error in any parameter was 9% with Bayesian Optimization and 28% with Nelder-Mead. Finally, the Bayesian Optimization algorithm was demonstrated to be robust to moderate levels of noise; when the standard deviation of the objective function was 16% of the mean, the maximum error in any parameter value was 16%, and the absolute mean difference in dose was 0.9% (profile) and 0.8% (depth-dose). CONCLUSIONS: An open-source library for optimization with Topas Monte Carlo has been developed, tested, and released. This tool will improve accuracy and efficiency in any situation in which the optimal value of a parameter in a Monte Carlo simulation is unknown. Applications for this tool include (1) The design of new components (2) Reverse engineering of models based on limited experimental or published data, and (3) Tuning of Monte Carlo "hyper parameters" such as variance reduction, physics settings, or scoring parameters.

Bayesian optimization to design a novel x-ray shaping device.

PURPOSE: In radiation therapy, x-ray dose must be precisely sculpted to the tumor, while simultaneously avoiding surrounding organs at risk. This requires modulation of x-ray intensity in space and/or time. Typically, this is achieved using a multi leaf collimator (MLC)-a complex mechatronic device comprising over one hundred individually powered tungsten 'leaves' that move in or out of the radiation field as required. Here, an all-electronic x-ray collimation concept with no moving parts is presented, termed "SPHINX": Scanning Pencil-beam High-speed Intensity-modulated X-ray source. SPHINX utilizes a spatially distributed bremsstrahlung target and collimator array in conjunction with magnetic scanning of a high energy electron beam to generate a plurality of small x-ray "beamlets." METHODS: A simulation framework was developed in Topas Monte Carlo incorporating a phase space electron source, transport through user defined magnetic fields, bremsstrahlung x-ray production, transport through a SPHINX collimator, and dose in water. This framework was completely parametric, meaning a simulation could be built and run for any supplied geometric parameters. This functionality was coupled with Bayesian optimization to find the best parameter set based on an objective function which included terms to maximize dose rate for a user defined beamlet width while constraining inter-channel cross talk and electron contamination. Designs for beamlet widths of 5, 7, and 10 mm2 were generated. Each optimization was run for 300 iterations and took approximately 40 h on a 24-core computer. For the optimized 7-mm model, a simulation of all beamlets in water was carried out including a linear scanning magnet calibration simulation. Finally, a back-of-envelope dose rate formalism was developed and used to estimate dose rate under various conditions. RESULTS: The optimized 5-, 7-, and 10-mm models had beamlet widths of 5.1 , 7.2 , and 10.1 mm2 and dose rates of 3574, 6351, and 10 015 Gy/C, respectively. The reduction in dose rate for smaller beamlet widths is a result of both increased collimation and source occlusion. For the simulation of all beamlets in water, the scanning magnet calibration reduced the offset between the collimator channels and beam centroids from 2.9 ±1.9 mm to 0.01 ±0.03 mm. A slight reduction in dose rate of approximately 2% per degree of scanning angle was observed. Based on a back-of-envelope dose rate formalism, SPHINX in conjunction with next-generation linear accelerators has the potential to achieve substantially higher dose rates than conventional MLC-based delivery, with delivery of an intensity modulated 100 × 100 mm2 field achievable in 0.9 to 10.6 s depending on the beamlet widths used. CONCLUSIONS: Bayesian optimization was coupled with Monte Carlo modeling to generate SPHINX geometries for various beamlet widths. A complete Monte Carlo simulation for one of these designs was developed, including electron beam transport of all beamlets through scanning magnets, x-ray production and collimation, and dose in water. These results demonstrate that SPHINX is a promising candidate for sculpting radiation dose with no moving parts, and has the potential to vastly improve both the speed and robustness of radiotherapy delivery. A multi-beam SPHINX system may be a candidate for delivering magavoltage FLASH RT in humans.

Intraoperative OCT for the assessment of posterior capsular integrity in pediatric cataract surgery.

PURPOSE: To study the morphology of the posterior lens cortex and posterior capsules (PCs) in pediatric patients with posterior lens opacities using intraoperative optical coherence tomography (iOCT). SETTING: Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China. DESIGN: Prospective observational study. METHODS: Pediatric patients with posterior lens opacities were imaged using iOCT during cataract surgery. The morphology of the posterior lens cortex and PC, along with the common patterns to indicate PC integrity, was assessed. Moreover, PC rent during surgery was observed. RESULTS: A total of 62 eyes from 53 patients were included. The mean age of patients was 3.8 years. 4 morphological variants of posterior lens opacity were observed: type I (34/62 [54.8%]) with an intact PC; type II (20/62 [32.3%]) with an intact PC, which protruded into the anterior vitreous; type III (3/62 [4.8%]) with a deficient PC and an inability to delineate the PC; and type IV (5/62 [8.1%]) with dense opacity and an inability to characterize the posterior cortex and PC. Phacoemulsification could be performed in types I and II. In types III and IV, manual nucleus removal was performed instead of phacoemulsification. 3 cases (100%) of type III PC dehiscence developed during surgery, whereas no cases developed PC dehiscence of other types. CONCLUSIONS: The morphology of the PC and posterior lens cortex in pediatric posterior lens opacities could be categorized, and PC integrity could be assessed using iOCT, which was useful to guide surgical strategies and increase safety in pre-existing PC dehiscence in pediatric cataract surgery.

An automated optimization strategy to design collimator geometry for small field radiation therapy systems.

Purpose. To develop an automated optimization strategy to facilitate collimator design for small-field radiotherapy systems.Methods and Materials.We developed an objective function that links the dose profile characteristics (FWHM, penumbra, and central dose rate) and the treatment head geometric parameters (collimator thickness/radii, source-to-distal-collimator distance (SDC)) for small-field radiotherapy systems. We performed optimization using a downhill simplex algorithm. We applied this optimization strategy to a linac-based radiosurgery system to determine the optimal geometry of four pencil-beam collimators to produce 5, 10, 15, and 20 mm diameter photon beams (from a 6.7 MeV, 2.1 mm FWHM electron beam). Two different optimizations were performed to prioritize minimum penumbra or maximum central dose rate for each beam size. We compared the optimized geometric parameters and dose distributions to an existing clinical system (CyberKnife).Results.When minimum penumbra was prioritized, using the same collimator thickness and SDC (40 cm) as a CyberKnife system, the optimized collimator upstream and downstream radii agreed with the CyberKnife system within 3%-14%, the optimized output factors agreed within 0%-8%, and the optimized transverse and percentage depth dose profiles matched those of the CyberKnife with the penumbras agreeing within 2%. However, when maximum dose rate was prioritized, allowing both the collimator thickness and SDC to change, the central dose rate for larger collimator sizes (10, 15, 20 mm) could be increased by about 1.5-2 times at the cost of 1.5-2 times larger penumbras. No further improvement in central dose rate for the 5 mm beam size could be achieved.Conclusions.We developed an automated optimization strategy to design the collimator geometry for small-field radiation therapy systems. Using this strategy, the penumbra-prioritized dose distribution and geometric parameters agree well with the CyberKnife system as an example, suggesting that this system was designed to prioritize sharp penumbra. This represents proof-of-principle that an automated optimization strategy may apply to more complex collimator designs with multiple optimization parameters.

[A Real-world Study on the Assessment of Pathological Characteristics and Targeted Therapeutic Effect of Non-small Cell Lung Cancer Patients with Positive Driving Genes and High PD-L1 Expression].

BACKGROUND: Targeted therapy for patients with driver genes positive and immunotherapy for patients with driver gene-negative but high programmed death ligand 1 (PD-L1) expression are the standards of first-line treatment for patients with advanced non-small cell lung cancer (NSCLC). The treatment options for patients with driver gene positive and high PD-L1 expression are still worth exploring. METHODS: The characteristics of 315 patients with NSCLC were identified to analyze the clinicopathological characteristics of patients with driver gene positive and high PD-L1 expression, and the efficacy of targeted therapy. RESULTS: Among the 315 patients, the total positive rate of driver genes was 62.2%, and the high PD-L1 expression rate (≥50.0%) was 11.2%. The proportion of patients with driver gene positive and high PD-L1 expression was 10.7%. PD-L1 was highly expressed in patients with epidermal growth factor receptor (EGFR) mutation, KRAS mutation, ALK fusion, BRAF mutation, and MET 14 exon skip mutation, the proportions were 7.8% (11/141), 18.2% (4/22), and 23.1%, (3/13), 50.0% (2/4) and 100.0% (1/1) respectively. EGFR mutation positive with PD-L1 high expression was mainly in patients with stage IV lung adenocarcinoma. KRAS mutation positive with PD-L1 high expression was mainly in patients with a history of smoking. Among them, two patients were followed in detail for targeted therapy, who with ALK fusion-positive and PD-L1 high expression (90.0%), EGFR L858R mutation and PD-L1 high expression (70.0%) respectively. The total OS of the patients was 5 months, 2 months. CONCLUSIONS: The high PD-L1 expression rate in NSCLC patients with different driver gene mutations was variable, which maybe correlated with distinct clinicopathological characteristics. Patients with sensitive mutations and high PD-L1 expression may be less benefit from targeted therapy and have poor prognosis.

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

BACKGROUND: Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial. METHODS: In this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient. RESULTS: Qualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method. CONCLUSION: Our study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.